Key Takeaways

  • 75% of U.S. employers use automated applicant tracking systems to screen resumes before a human reviews them (Harvard Business School & Accenture, 2021)
  • The most common ATS failures are missing keywords, incompatible formatting, and incorrect file types
  • ResumeGeni scores your resume across 8 parsing layers — modeled on the same steps enterprise ATS platforms like Workday, Greenhouse, and Taleo use to evaluate candidates

How ATS Resume Scoring Works

Applicant tracking systems parse your resume into structured data — extracting your name, contact info, work history, skills, and education — then score how well that data matches the job requirements. Many ATS rejections happen because the parser couldn't extract critical fields, not because the candidate wasn't qualified.

LayerWhat It ChecksWhy It Matters
Document extractionFile format, encoding, readabilityCorrupted or image-only PDFs fail immediately
Layout analysisTables, columns, headers, footersMulti-column layouts break field extraction
Section detectionExperience, education, skills headingsNon-standard headings cause sections to be missed
Field mappingName, email, phone, dates, titlesMissing contact info is a common cause of immediate rejection
Keyword matchingJob-specific terms, skills, certificationsKeyword overlap affects recruiter search visibility and ATS scoring
Chronology checkDate ordering, gap detectionReverse-chronological order is expected by most ATS
QuantificationMetrics, numbers, measurable outcomesQuantified achievements help human reviewers and some scoring models
Confidence scoringOverall parse quality and completenessLow-confidence parses get deprioritized in results

Frequently Asked Questions

Is ResumeGeni free?
Yes. ResumeGeni is currently in beta — ATS analysis, scoring, and initial improvement suggestions are free with no signup required. Full guidance and saved reports may require a free account.
What file formats are supported?
PDF, DOCX, DOC, TXT, RTF, ODT, and Apple Pages. PDF and DOCX are recommended for best ATS compatibility.
How is the ATS score calculated?
Your resume is processed through an 8-layer parsing pipeline that extracts structured data the same way enterprise ATS platforms do. The score reflects how completely and accurately your resume can be parsed, plus how well your content matches common ATS ranking criteria.
Can ATS read PDF resumes?
Yes, but not all PDFs are equal. Text-based PDFs parse well. Image-only PDFs (scanned documents) and PDFs with complex tables or multi-column layouts often fail ATS parsing. Our analyzer will flag these issues.
How do I improve my ATS score?
Focus on three areas: use a clean single-column format, include keywords from the job description naturally in your experience bullets, and ensure all sections (contact, experience, education, skills) use standard headings.

ATS Guides & Resources

Built by engineers with 12 years of experience building enterprise hiring technology at ZipRecruiter. Last updated .

Ramp - Early Career Software Engineer

Silverchair · Remote

About Ramp

Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $100B in annualized spend flows in and out of 50,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books.

The problems are high-stakes, data-dense, and unforgiving.

We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome.

The median Ramp customer saves 5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same.

If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it.

About the Role

The team owns the core experience that helps finance teams control, automate, and optimize company spend. We build the engine that powers Ramp’s card and expense workflows—from card issuance and spend limits to approvals, policy enforcement, and real-time insights.


Our systems handle billions of dollars in transactions and integrate deeply with Ramp’s AI platform, financial infrastructure, and partner ecosystems (banks, ERPs, HRIS).


As a backend engineer on this team, you’ll work across product surfaces that are central to Ramp’s success: cards, approvals, spend controls, and automation intelligence.


What You’ll Do

  • Design, build, and scale backend systems that power spend controls, approval workflows, and card transactions at massive scale

  • Collaborate cross-functionally with product, design, and data to deliver intelligent, user-first experiences for finance teams

  • Integrate with Ramp’s internal AI platform to automate spend policy enforcement and anomaly detection

  • Own complex projects end-to-end — from architecture to deployment and observability

  • Partner with the Product Platform and Integrations teams to ensure reliability, scalability, and data consistency across financial systems

  • Continuously improve Ramp’s transaction and policy infrastructure for performance, accuracy, and resilience


What We’re Looking For

  • 1+ years of software engineering experience (backend or fullstack with a backend focus)

  • Strong fundamentals in backend systems and API design — experience with Python, Go, or TypeScript is ideal

  • Familiarity with PostgreSQL, AWS, and modern observability tooling

  • Product sense: ability to balance technical quality with speed and user value

  • Collaborative, thoughtful communicator who thrives in high-ownership environments


Nice to Have

  • Fintech or payments experience (cards, reconciliation, transaction processing)

  • Exposure to AI- or rule-based automation systems

  • Experience integrating with ERP or accounting systems

  • Familiarity with security, risk, or compliance-driven domains

  • Experience building reliable distributed systems that process large volumes of financial or transactional data

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